Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3676
Missing cells9524
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory609.9 B

Variable types

Text3
Numeric10
Categorical10

Alerts

property_type has constant value "house" Constant
area is highly overall correlated with built_up_area and 3 other fieldsHigh correlation
bathroom is highly overall correlated with bedRoom and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with bathroom and 3 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 6 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 6 other fieldsHigh correlation
store room is highly imbalanced (55.7%) Imbalance
facing has 1044 (28.4%) missing values Missing
property_type has 2817 (76.6%) missing values Missing
super_built_up_area has 1802 (49.0%) missing values Missing
built_up_area has 1986 (54.0%) missing values Missing
carpet_area has 1804 (49.1%) missing values Missing
area is highly skewed (γ1 = 21.81519917) Skewed
built_up_area is highly skewed (γ1 = 40.70657243) Skewed
carpet_area is highly skewed (γ1 = 24.33323911) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 462 (12.6%) zeros Zeros

Reproduction

Analysis started2025-01-28 00:40:57.557796
Analysis finished2025-01-28 00:41:29.113089
Duration31.56 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size293.9 KiB
2025-01-28T06:11:29.647860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.869388
Min length1

Characters and Unicode

Total characters61995
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowambience creacions
2nd rowm3m woodshire
3rd rowsatya the legend
4th rowvatika gurgaon
5th rowdlf the arbour
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 219
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.6%
heights 134
 
1.4%
Other values (783) 7495
77.5%
2025-01-28T06:11:30.684201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6709
 
10.8%
6001
 
9.7%
a 5860
 
9.5%
r 4169
 
6.7%
n 4162
 
6.7%
i 3830
 
6.2%
t 3717
 
6.0%
s 3472
 
5.6%
l 2941
 
4.7%
o 2752
 
4.4%
Other values (31) 18382
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6709
 
10.8%
6001
 
9.7%
a 5860
 
9.5%
r 4169
 
6.7%
n 4162
 
6.7%
i 3830
 
6.2%
t 3717
 
6.0%
s 3472
 
5.6%
l 2941
 
4.7%
o 2752
 
4.4%
Other values (31) 18382
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6709
 
10.8%
6001
 
9.7%
a 5860
 
9.5%
r 4169
 
6.7%
n 4162
 
6.7%
i 3830
 
6.2%
t 3717
 
6.0%
s 3472
 
5.6%
l 2941
 
4.7%
o 2752
 
4.4%
Other values (31) 18382
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6709
 
10.8%
6001
 
9.7%
a 5860
 
9.5%
r 4169
 
6.7%
n 4162
 
6.7%
i 3830
 
6.2%
t 3717
 
6.0%
s 3472
 
5.6%
l 2941
 
4.7%
o 2752
 
4.4%
Other values (31) 18382
29.7%

price
Real number (ℝ)

High correlation 

Distinct285
Distinct (%)7.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.1532823
Minimum0.01
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:31.481630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.6
median1
Q32
95-th percentile8.4
Maximum31.5
Range31.49
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation3.0598228
Coefficient of variation (CV)1.421004
Kurtosis14.466122
Mean2.1532823
Median Absolute Deviation (MAD)0.93
Skewness3.2284189
Sum7878.86
Variance9.3625155
MonotonicityNot monotonic
2025-01-28T06:11:31.816960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1224
33.3%
2 440
 
12.0%
0.09 166
 
4.5%
3 165
 
4.5%
0.08 126
 
3.4%
0.07 103
 
2.8%
0.03 101
 
2.7%
0.04 97
 
2.6%
0.05 86
 
2.3%
0.02 85
 
2.3%
Other values (275) 1066
29.0%
ValueCountFrequency (%)
0.01 3
 
0.1%
0.02 85
2.3%
0.03 101
2.7%
0.04 97
2.6%
0.05 86
2.3%
0.06 81
2.2%
0.07 103
2.8%
0.08 126
3.4%
0.09 166
4.5%
0.22 1
 
< 0.1%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

sector
Text

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.8 KiB
2025-01-28T06:11:32.302143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3210011
Min length7

Characters and Unicode

Total characters34264
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 22
2nd rowsector 107
3rd rowsector 57
4th rowsector 83
5th rowsector 63
ValueCountFrequency (%)
sector 3451
46.8%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 88
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (106) 2915
39.5%
2025-01-28T06:11:33.130122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3806
11.1%
3704
10.8%
s 3696
10.8%
r 3696
10.8%
e 3541
10.3%
c 3502
10.2%
t 3462
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 780
 
2.3%
Other values (21) 6198
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3806
11.1%
3704
10.8%
s 3696
10.8%
r 3696
10.8%
e 3541
10.3%
c 3502
10.2%
t 3462
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 780
 
2.3%
Other values (21) 6198
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3806
11.1%
3704
10.8%
s 3696
10.8%
r 3696
10.8%
e 3541
10.3%
c 3502
10.2%
t 3462
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 780
 
2.3%
Other values (21) 6198
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3806
11.1%
3704
10.8%
s 3696
10.8%
r 3696
10.8%
e 3541
10.3%
c 3502
10.2%
t 3462
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 780
 
2.3%
Other values (21) 6198
18.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.5%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13890.811
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:33.474868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.9
Q16816.5
median9020
Q313876.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7060

Descriptive statistics

Standard deviation23212.968
Coefficient of variation (CV)1.6711023
Kurtosis186.88875
Mean13890.811
Median Absolute Deviation (MAD)2793
Skewness11.436264
Sum50826479
Variance5.3884186 × 108
MonotonicityNot monotonic
2025-01-28T06:11:33.809149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
22222 13
 
0.4%
11111 13
 
0.4%
6666 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3508
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct2418
Distinct (%)66.1%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean10436.018
Minimum50
Maximum750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:34.152139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile466
Q11344.5
median9014
Q314517.5
95-th percentile26785
Maximum750000
Range749950
Interquartile range (IQR)13173

Descriptive statistics

Standard deviation22962.023
Coefficient of variation (CV)2.2002667
Kurtosis597.66783
Mean10436.018
Median Absolute Deviation (MAD)7350
Skewness21.815199
Sum38185389
Variance5.2725449 × 108
MonotonicityNot monotonic
2025-01-28T06:11:34.572318image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3240 43
 
1.2%
2700 35
 
1.0%
900 34
 
0.9%
1800 29
 
0.8%
1350 22
 
0.6%
2250 18
 
0.5%
4500 17
 
0.5%
4518 16
 
0.4%
12500 15
 
0.4%
2430 14
 
0.4%
Other values (2408) 3416
92.9%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
750000 1
< 0.1%
600000 1
< 0.1%
571429 1
< 0.1%
555556 1
< 0.1%
215517 1
< 0.1%
145033 1
< 0.1%
98978 1
< 0.1%
94118 1
< 0.1%
93333 1
< 0.1%
90009 1
< 0.1%
Distinct2355
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Memory size428.1 KiB
2025-01-28T06:11:35.335305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.240479
Min length12

Characters and Unicode

Total characters199388
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1850 ?
Unique (%)50.3%

Sample

1st rowCarpet area: 3000 (278.71 sq.m.)
2nd rowSuper Built up area 1534(142.51 sq.m.)Carpet area: 1056 sq.ft. (98.11 sq.m.)
3rd rowPlot area 642(536.79 sq.m.)Built Up area: 630 sq.yards (526.76 sq.m.)Carpet area: 620 sq.yards (518.4 sq.m.)
4th rowSuper Built up area 1245(115.66 sq.m.)Built Up area: 850 sq.ft. (78.97 sq.m.)Carpet area: 790 sq.ft. (73.39 sq.m.)
5th rowBuilt Up area: 3950 (366.97 sq.m.)
ValueCountFrequency (%)
area 5572
18.5%
sq.m 3654
12.1%
up 3019
 
10.0%
built 2315
 
7.7%
super 1874
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8699
28.9%
2025-01-28T06:11:36.349015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26459
 
13.3%
. 20386
 
10.2%
a 13152
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9205
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6767
 
3.4%
Other values (25) 82328
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26459
 
13.3%
. 20386
 
10.2%
a 13152
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9205
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6767
 
3.4%
Other values (25) 82328
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26459
 
13.3%
. 20386
 
10.2%
a 13152
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9205
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6767
 
3.4%
Other values (25) 82328
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26459
 
13.3%
. 20386
 
10.2%
a 13152
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9205
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6767
 
3.4%
Other values (25) 82328
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3599021
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:36.803325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8978577
Coefficient of variation (CV)0.56485506
Kurtosis18.2097
Mean3.3599021
Median Absolute Deviation (MAD)1
Skewness3.4850949
Sum12351
Variance3.6018638
MonotonicityNot monotonic
2025-01-28T06:11:37.088778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 942
25.6%
4 659
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 659
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4243743
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:37.484537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.94831
Coefficient of variation (CV)0.56895357
Kurtosis17.538735
Mean3.4243743
Median Absolute Deviation (MAD)1
Skewness3.2487382
Sum12588
Variance3.7959119
MonotonicityNot monotonic
2025-01-28T06:11:37.813518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 819
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 819
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
3+
1171 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3185528
Min length1

Characters and Unicode

Total characters4847
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row3
3rd row3+
4th row2
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1171
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
5.0%

Length

2025-01-28T06:11:38.191929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:38.462888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 2245
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
5.0%

Most occurring characters

ValueCountFrequency (%)
3 2245
46.3%
+ 1171
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2245
46.3%
+ 1171
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2245
46.3%
+ 1171
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2245
46.3%
+ 1171
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7976483
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:38.759817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0131662
Coefficient of variation (CV)0.88459506
Kurtosis4.5145653
Mean6.7976483
Median Absolute Deviation (MAD)3
Skewness1.6938478
Sum24859
Variance36.158167
MonotonicityNot monotonic
2025-01-28T06:11:39.073527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 160
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 160
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1044
Missing (%)28.4%
Memory size233.6 KiB
North-East
623 
East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth-East
2nd rowNorth-East
3rd rowNorth-East
4th rowSouth-East
5th rowEast

Common Values

ValueCountFrequency (%)
North-East 623
16.9%
East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.3%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1044
28.4%

Length

2025-01-28T06:11:39.389018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:39.658978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
north-east 623
23.7%
east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
Relatively New
1646 
New Property
593 
Moderately Old
562 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.385745
Min length9

Characters and Unicode

Total characters49206
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Property
2nd rowRelatively New
3rd rowRelatively New
4th rowRelatively New
5th rowUndefined

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 562
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 266
 
7.2%

Length

2025-01-28T06:11:39.958658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:40.236294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.4%
property 896
12.7%
old 865
 
12.3%
moderately 562
 
8.0%
undefined 306
 
4.3%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3636
 
7.4%
3370
 
6.8%
y 3104
 
6.3%
r 2886
 
5.9%
d 2305
 
4.7%
N 2239
 
4.6%
w 2239
 
4.6%
i 2218
 
4.5%
Other values (15) 14061
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3636
 
7.4%
3370
 
6.8%
y 3104
 
6.3%
r 2886
 
5.9%
d 2305
 
4.7%
N 2239
 
4.6%
w 2239
 
4.6%
i 2218
 
4.5%
Other values (15) 14061
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3636
 
7.4%
3370
 
6.8%
y 3104
 
6.3%
r 2886
 
5.9%
d 2305
 
4.7%
N 2239
 
4.6%
w 2239
 
4.6%
i 2218
 
4.5%
Other values (15) 14061
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3636
 
7.4%
3370
 
6.8%
y 3104
 
6.3%
r 2886
 
5.9%
d 2305
 
4.7%
N 2239
 
4.6%
w 2239
 
4.6%
i 2218
 
4.5%
Other values (15) 14061
28.6%

property_type
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing2817
Missing (%)76.6%
Memory size190.8 KiB
house
859 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4295
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhouse
2nd rowhouse
3rd rowhouse
4th rowhouse
5th rowhouse

Common Values

ValueCountFrequency (%)
house 859
 
23.4%
(Missing) 2817
76.6%

Length

2025-01-28T06:11:40.519940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:40.737783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
house 859
100.0%

Most occurring characters

ValueCountFrequency (%)
h 859
20.0%
o 859
20.0%
u 859
20.0%
s 859
20.0%
e 859
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 859
20.0%
o 859
20.0%
u 859
20.0%
s 859
20.0%
e 859
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 859
20.0%
o 859
20.0%
u 859
20.0%
s 859
20.0%
e 859
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 859
20.0%
o 859
20.0%
u 859
20.0%
s 859
20.0%
e 859
20.0%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1924.6657
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:41.006135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.25
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.75

Descriptive statistics

Standard deviation763.97466
Coefficient of variation (CV)0.39693888
Kurtosis10.373606
Mean1924.6657
Median Absolute Deviation (MAD)372
Skewness1.8396359
Sum3606823.5
Variance583657.29
MonotonicityNot monotonic
2025-01-28T06:11:41.361561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1633
44.4%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct643
Distinct (%)38.0%
Missing1986
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:41.682418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2025-01-28T06:11:42.018901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (633) 1387
37.7%
(Missing) 1986
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct732
Distinct (%)39.1%
Missing1804
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1796
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:42.345655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147163
Kurtosis604.53764
Mean2529.1796
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624.2
Variance5.1983254 × 108
MonotonicityNot monotonic
2025-01-28T06:11:42.672465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (722) 1578
42.9%
(Missing) 1804
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
2971 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Length

2025-01-28T06:11:42.968747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:43.195497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
2349 
1
1327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2349
63.9%
1 1327
36.1%

Length

2025-01-28T06:11:43.593053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:43.823064image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
63.9%
1 1327
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2349
63.9%
1 1327
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2349
63.9%
1 1327
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2349
63.9%
1 1327
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2349
63.9%
1 1327
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
3338 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Length

2025-01-28T06:11:44.068068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:44.311041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
3020 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Length

2025-01-28T06:11:44.556098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:44.782791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
3271 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Length

2025-01-28T06:11:45.021866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:45.260683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
1
2403 
2
1063 
0
 
210

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 2403
65.4%
2 1063
28.9%
0 210
 
5.7%

Length

2025-01-28T06:11:45.498294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T06:11:45.737938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 2403
65.4%
2 1063
28.9%
0 210
 
5.7%

Most occurring characters

ValueCountFrequency (%)
1 2403
65.4%
2 1063
28.9%
0 210
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2403
65.4%
2 1063
28.9%
0 210
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2403
65.4%
2 1063
28.9%
0 210
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2403
65.4%
2 1063
28.9%
0 210
 
5.7%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.530468
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2025-01-28T06:11:46.006343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.055626
Coefficient of variation (CV)0.74172067
Kurtosis-0.8802774
Mean71.530468
Median Absolute Deviation (MAD)38
Skewness0.45875761
Sum262946
Variance2814.8995
MonotonicityNot monotonic
2025-01-28T06:11:46.342958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
42 45
 
1.2%
37 45
 
1.2%
Other values (151) 2312
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 40
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-01-28T06:11:24.336884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:00.478918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:03.105654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:06.145445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:09.008031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:11.668906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:14.235022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:16.847652image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:19.384359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:21.902033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:24.733866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:00.726684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:03.409269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:06.425148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:09.282379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:11.906834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:14.475976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:17.072425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:19.630121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:22.150668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:25.031651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:00.976167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:03.705957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:06.711436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:09.528813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:12.151768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:14.718031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:17.336418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:19.881067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:22.396380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:25.295843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:01.233655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:04.139990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:07.003405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:09.911108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:12.406078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:15.062184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:17.553897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:20.146657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:22.634257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:25.560750image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:01.485502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:04.522107image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:07.376755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:10.183221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:12.665816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:15.417910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:17.818236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:20.416628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:22.887920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:25.827028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:01.733506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:04.860650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:07.769155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:10.452089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:12.959574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:15.676011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:18.054268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:20.695843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:23.145104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:26.075014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:01.958890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:05.092001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:08.049177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:10.683325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:13.215293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:15.897581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:18.279518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:20.936284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:23.378619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:26.328075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:02.183482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:05.333166image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:08.271322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:10.924138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:13.452256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:16.125290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:18.513874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:21.157871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:23.619980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:26.580392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:02.557406image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:05.588675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:08.526218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:11.178753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:13.708018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:16.376548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:18.888124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:21.409924image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:23.837052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:26.837455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:02.793971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:05.831750image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:08.756950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:11.421808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:13.957437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:16.607477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:19.128960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:21.638669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-28T06:11:24.076823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-01-28T06:11:46.608478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2740.1110.1300.0000.0000.0920.1250.2150.2560.1080.1870.1120.0560.2860.1430.1400.085
area0.0001.0000.0140.4120.3080.5770.7750.0520.3170.0420.3510.0640.0400.5100.0950.0260.0000.0160.818
balcony0.2740.0141.0000.2250.1750.0000.0260.0160.0790.1790.2240.0820.1970.1240.0330.4410.1460.1830.306
bathroom0.1110.4120.2251.0000.8620.4650.5990.044-0.0050.2000.1800.0700.2860.7040.4110.5190.2440.1760.819
bedRoom0.1300.3080.1750.8621.0000.3800.5690.032-0.1040.1690.0570.0790.2910.6870.4170.3170.2230.1540.799
built_up_area0.0000.5770.0000.4650.3801.0000.9691.0000.0910.0870.2890.0000.0000.5630.1320.0000.0000.0000.926
carpet_area0.0000.7750.0260.5990.5690.9691.0000.0000.1590.0000.2390.0160.0000.5830.1360.0000.0000.0030.894
facing0.0920.0520.0160.0440.0321.0000.0001.0000.0000.0500.0650.0000.0290.0220.0000.0360.0360.0000.000
floorNum0.1250.3170.079-0.005-0.1040.0910.1590.0001.0000.0200.2320.0330.102-0.052-0.1260.0830.1120.0780.151
furnishing_type0.2150.0420.1790.2000.1690.0870.0000.0500.0201.0000.2440.0610.2170.1650.0220.2730.1570.1430.133
luxury_score0.2560.3510.2240.1800.0570.2890.2390.0650.2320.2441.0000.1760.1890.1580.0550.3480.2280.1830.223
others0.1080.0640.0820.0700.0790.0000.0160.0000.0330.0610.1761.0000.0330.0380.0360.0000.1060.0310.084
pooja room0.1870.0400.1970.2860.2910.0000.0000.0290.1020.2170.1890.0331.0000.3490.0430.2520.3050.3130.157
price0.1120.5100.1240.7040.6870.5630.5830.022-0.0520.1650.1580.0380.3491.0000.7460.3180.3220.2550.736
price_per_sqft0.0560.0950.0330.4110.4170.1320.1360.000-0.1260.0220.0550.0360.0430.7461.0000.0440.0000.0300.286
servant room0.2860.0260.4410.5190.3170.0000.0000.0360.0830.2730.3480.0000.2520.3180.0441.0000.1610.1850.584
store room0.1430.0000.1460.2440.2230.0000.0000.0360.1120.1570.2280.1060.3050.3220.0000.1611.0000.2260.046
study room0.1400.0160.1830.1760.1540.0000.0030.0000.0780.1430.1830.0310.3130.2550.0300.1850.2261.0000.120
super_built_up_area0.0850.8180.3060.8190.7990.9260.8940.0000.1510.1330.2230.0840.1570.7360.2860.5840.0460.1201.000

Missing values

2025-01-28T06:11:27.405826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-28T06:11:28.106125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-28T06:11:28.839344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

societypricesectorprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionproperty_typesuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0ambience creacions6.00sector 2220533.029221.0Carpet area: 3000 (278.71 sq.m.)453+10.0South-EastNew PropertyNaNNaNNaN3000.001010249
1m3m woodshire1.00sector 1077496.013340.0Super Built up area 1534(142.51 sq.m.)Carpet area: 1056 sq.ft. (98.11 sq.m.)2230.0North-EastRelatively NewNaN1534.0NaN1056.010000129
2satya the legend9.25sector 5716577.05580.0Plot area 642(536.79 sq.m.)Built Up area: 630 sq.yards (526.76 sq.m.)Carpet area: 620 sq.yards (518.4 sq.m.)553+4.0North-EastRelatively NewhouseNaN630.0620.0111102160
3vatika gurgaon0.07sector 836265.01117.0Super Built up area 1245(115.66 sq.m.)Built Up area: 850 sq.ft. (78.97 sq.m.)Carpet area: 790 sq.ft. (73.39 sq.m.)2223.0South-EastRelatively NewNaN1245.0850.0790.0100102165
4dlf the arbour8.50sector 6321519.03950.0Built Up area: 3950 (366.97 sq.m.)443+27.0NaNUndefinedhouseNaN3950.0NaN00000161
5m3m golfestate1.00sector 7910000.010000.0Carpet area: 1400 (130.06 sq.m.)2224.0EastUndefinedNaNNaNNaN1400.011100083
6bajrang apartments0.03sector 64470.0671.0Carpet area: 850 (78.97 sq.m.)2221.0NaNModerately OldNaNNaNNaN850.00000010
7signature global city 810.75sector 816849.01095.0Plot area 1095(101.73 sq.m.)3234.0EastUnder ConstructionhouseNaN1095.0NaN10000197
8microtek greenburg1.00sector 868446.011840.0Super Built up area 2285(212.28 sq.m.)34312.0EastRelatively NewNaN2285.0NaNNaN01000172
9smart world orchard2.00sector 6114785.013527.0Built Up area: 1630 (151.43 sq.m.)Carpet area: 1625 sq.ft. (150.97 sq.m.)3332.0EastNew PropertyNaNNaN1630.01625.000000261
societypricesectorprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionproperty_typesuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793unitech aspen greens6.80sector 5031481.02160.0Plot area 240(200.67 sq.m.)Built Up area: 220 sq.yards (183.95 sq.m.)Carpet area: 200 sq.yards (167.23 sq.m.)4533.0WestOld PropertyhouseNaN220.0200.0110002111
3794emaar mgf the palm drive3.00sector 6614285.021001.0Super Built up area 2100(195.1 sq.m.)453+10.0EastRelatively NewNaN2100.0NaNNaN01000249
3795emaar mgf emerald floors premier2.00sector 6514303.013983.0Super Built up area 1650(153.29 sq.m.)3330.0North-EastRelatively NewNaN1650.0NaNNaN110002159
3796not applicable0.40sector 49259.0432.0Plot area 432(40.13 sq.m.)1111.0NaNModerately OldhouseNaN432.0NaN0000010
3797international city by sobha phase 112.00sector 10920000.06000.0Plot area 8000(743.22 sq.m.)Built Up area: 7000 sq.ft. (650.32 sq.m.)Carpet area: 6000 sq.ft. (557.42 sq.m.)5632.0North-EastRelatively NewhouseNaN7000.06000.0110002154
3798sare crescent parc0.07sector 925556.01260.0Built Up area: 140 (117.06 sq.m.)33214.0NaNUnder ConstructionNaNNaN140.0NaN00000138
3799emaar mgf the enclave2.00sector 6612352.016192.0Super Built up area 1920(178.37 sq.m.)Built Up area: 1800 sq.ft. (167.23 sq.m.)Carpet area: 1700 sq.ft. (157.94 sq.m.)34212.0North-EastRelatively NewNaN1920.01800.01700.0010002156
3800guru gram haryana cghs1.00sector 567227.013837.0Super Built up area 1920(178.37 sq.m.)3334.0NorthModerately OldNaN1920.0NaNNaN100002134
3801ss the leaf1.00sector 857641.013087.0Super Built up area 2408(223.71 sq.m.)Carpet area: 1685 sq.ft. (156.54 sq.m.)3438.0South-EastRelatively NewNaN2408.0NaN1685.0000101158
3802vatika lifestyle homes1.00sector 835699.017547.0Super Built up area 1755(163.04 sq.m.)Built Up area: 1500 sq.ft. (139.35 sq.m.)Carpet area: 1100 sq.ft. (102.19 sq.m.)3222.0South-WestModerately OldNaN1755.01500.01100.000000195